MétaCan
Menu
Back to cohort
Record W2783949499

Modelling the surface mass balance of the Greenland ice sheet and neighbouring ice caps: A dynamical and statistical downscaling approach

2018· dissertation· en· W2783949499 on OpenAlexaboutno aff
Brice Noël

Bibliographic record

VenueORBi (University of Liège) · 2018
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsGreenland ice sheetMeltwaterGlacier mass balanceIce sheetSnowGlacierClimatologyDownscalingCryosphereEnvironmental scienceGlacier ice accumulationIce streamGeologyClimate modelSea iceAtmospheric sciencesClimate changeGeomorphologyOceanography
DOInot available

Abstract

fetched live from OpenAlex

The Greenland ice sheet (GrIS) is the world’s second largest ice mass, storing about one tenth of the Earth’s freshwater. If totally melted, global sea level would rise by 7.4 m, affecting low-lying regions worldwide. Since the mid-1990s, increased atmospheric and oceanic temperatures have accelerated GrIS mass loss through increased meltwater runoff and ice discharge from marine-terminating glaciers. To understand the causes of recent GrIS surface mass loss, we use the Regional Atmospheric Climate Model RACMO2. This meteorological model simulates the GrIS surface mass balance (SMB), i.e. the difference between snowfall accumulation and ablation from meltwater runoff. To cover a large domain at reasonable computational cost, RACMO2 is run at a relatively coarse horizontal resolution of 11 km (1958-2016). At this spatial resolution, the model does not well resolve small glaciated bodies, such as narrow glaciers and small peripheral ice caps (GICs), detached from the main ice sheet. Therefore, we developed a statistical downscaling algorithm that reprojects the RACMO2 output on a 1 km grid. This downscaled product allows to quantify mass changes of small ice masses in unprecedented detail. Using the downscaled data set, we identify 1997 as a tipping point for the mass balance of Greenland’s GICs. The GICs are located in relatively dry regions where summer melt nominally exceeds winter snowfall. To sustain these ice caps, the refreezing of meltwater in the snow is a key process. The snow acts as a ”sponge” that buffers a large fraction of meltwater, which subsequently refreezes in winter. The remaining meltwater runs off to the ocean and directly contributes to mass loss. Until 1997, the snow layer in the interior of these GICs could compensate for increased melt by refreezing more meltwater. Around 1997, following decades of increased melt, the snow became saturated with refrozen meltwater, so that any additional summer melt was forced to run off to the ocean, tripling the mass loss. We call this a tipping point, as it would take decades to regrow a new, healthy snow layer that could buffer enough summer meltwater. As a result, Greenland’s GICs are expected to undergo irreversible mass loss in the future. Similar mechanisms are at play in the Canadian Arctic Archipelago. While the northern ice caps, that are larger and more elevated, can still efficiently buffer meltwater in their extensive snow-covered accumulation zones, the southern smaller and lower-lying ice fields have already lost most of their meltwater retention capacity, causing uninterrupted mass loss during the last six decades. Consequently, these southern ice caps are expected to disappear within the next 400 years. For now, the main Greenland ice sheet is still safe, as porous snow in the extensive accumulation zone, covering about 90% of the GrIS, still buffers most of the summer melt. At the current rate of mass loss, it would still take 10,000 years to melt the GrIS completely. However, the tipping point reached for the peripheral GICs must be regarded as an alarm-signal for the GrIS in the near future, if temperatures continue to increase.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.190
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueORBi (University of Liège)Same topicCryospheric studies and observationsFrench-language works237,207